A RIVIEW ON DIGITAL TWINS IN PERSONALIZED MEDICINE: A FUTURE IN PHARMA CARE

Digital twin technology represents a transformative advancement in healthcare and pharmaceutical sciences. A digital twin is a dynamic virtual model of a patient, organ, or biological system that continuously integrates data from electronic health records, genomics, imaging, and wearable sensors. By combining artificial intelligence, machine learning, and Internet of Things (IoT) technologies, digital twins enable real-time simulations that can predict disease progression, optimize drug dosing, and support clinical decision-making. In personalized medicine, digital twins play a pivotal role in tailoring therapies to individual patient needs. Recent applications in cardiology, oncology, diabetes management, and pharmacogenomics demonstrate their ability to improve therapeutic outcomes while minimizing adverse drug reactions. Notably, digital twins have been explored in simulating treatment responses for drugs such as warfarin, insulin, cisplatin, and adalimumab, thereby ensuring safer and more precise dosing strategies. Beyond clinical applications, digital twins contribute to hospital operations, clinical trial design, and critical care management, making healthcare systems more predictive, preventive, and patient-centered. However, challenges remain regarding data privacy, ethical considerations, interoperability, and regulatory approval. Regulatory agencies such as the FDA and EMA are actively working toward establishing frameworks for the safe and effective integration of this technology. Overall, digital twins bridge the gap between computational modeling and real-world clinical practice. By enhancing precision medicine and pharmaceutical care, they hold the potential to revolutionize the future of healthcare, paving the way toward more efficient, individualized, and sustainable treatment strategies.